The human oral microbiome refers to an ecological community of symbiotic and pathogenic microorganisms found in the oral cavity. The oral cavity is an environment that provides various biological niches, such as the teeth, tongue, and oral mucosa. The oral cavity is the gateway between the external environment and the human body, maintaining oral homeostasis, protecting the mouth, and preventing disease. On the flip side, the oral microbiome also plays an important role in the triggering, development, and progression of oral and systemic diseases. In recent years, disease diagnosis through the analysis of the human oral microbiome has been realized with the recent development of innovative detection technology and is overwhelmingly promising compared to the previous era. It has been found that patients with oral and systemic diseases have variations in their oral microbiome compared to normal subjects. This narrative review provides insight into the pathophysiological role that the oral microbiome plays in influencing oral and systemic diseases and furthers the knowledge related to the oral microbiome produced over the past 30 years. A wide range of updates were provided with the latest knowledge of the oral microbiome to help researchers and clinicians in both academic and clinical aspects. The microbial community information can be utilized in non-invasive diagnosis and can help to develop a new paradigm in precision medicine, which will benefit human health in the era of post-metagenomics.

Exosomes are nanosized vesicles (30–140 nm) of endocytic origin that play important roles in regenerative medicine. They are derived from cell membranes during endocytic internalization and stabilize in biological fluids such as blood and synovia. Temporomandibular joint osteoarthritis (TMJ OA) is a degenerative disease, which, in addition to chronic pain, is characterized by progressive cartilage breakdown, condylar bone remodeling, and synovitis. However, traditional clinical treatments have limited symptom- and structure-modifying effects to restore damaged cartilage and other TMJ tissues. This is due to the limited self-healing capacity of condylar cartilage. Recently, stem-cell-derived exosomes have been studied as an alternative therapeutic approach to tissue repair and regeneration. It is known that trophic regulation of mesenchymal stem cells (MSCs) has anti-inflammatory and immunomodulatory effects under pathological conditions, and research on MSC-derived exosomes is rapidly accumulating. MSC-derived exosomes mimic the major therapeutic effects of MSCs. They affect the activity of immune effector cells and possess multilineage differentiation potential, including chondrogenic and osteogenic differentiation. Furthermore, exosomes are capable of regenerating cartilage or osseous compartments and restoring injured tissues and can treat dysfunction and pain caused by TMJ OA. In this review, we looked at the uniqueness of TMJ, the pathogenesis of TMJ OA, and the potential role of MSC-derived exosomes for TMJ cartilage and bone regeneration.

Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.

This study investigated the usefulness of deep learning-based automatic detection of anterior disc displacement (ADD) from magnetic resonance imaging (MRI) of patients with temporomandibular joint disorder (TMD). Sagittal MRI images of 2520 TMJs were collected from 861 men and 399 women (average age 37.33 ± 18.83 years). A deep learning algorithm with a convolutional neural network was developed. Data augmentation and the Adam optimizer were applied to reduce the risk of overfitting the deep-learning model. The prediction performances were compared between the models and human experts based on areas under the curve (AUCs). The fine-tuning model showed excellent prediction performance (AUC = 0.8775) and acceptable accuracy (approximately 77%). Comparing the AUC values of the from-scratch (0.8269) and freeze models (0.5858) showed lower performances of the other models compared to the fine-tuning model. In Grad-CAM visualizations, the fine-tuning scheme focused more on the TMJ disc when judging ADD, and the sparsity was higher than that of the from-scratch scheme (84.69% vs. 55.61%, p < 0.05). The three fine-tuned ensemble models using different data augmentation techniques showed a prediction accuracy of 83%. Moreover, the AUC values of ADD were higher when patients with TMD were divided by age (0.8549–0.9275) and sex (male: 0.8483, female: 0.9276). While the accuracy of the ensemble model was higher than that of human experts, the difference was not significant (p = 0.1987–0.0671). Learning from pre-trained weights allowed the fine-tuning model to outperform the from-scratch model. Another benefit of the fine-tuning model for diagnosing ADD of TMJ in Grad-CAM analysis was the deactivation of unwanted gradient values to provide clearer visualizations compared to the from-scratch model. The Grad-CAM visualizations also agreed with the model learned through important features in the joint disc area. The accuracy was further improved by an ensemble of three fine-tuning models using diversified data. The main benefits of this model were the higher specificity compared to human experts, which may be useful for preventing true negative cases, and the maintenance of its prediction accuracy across sexes and ages, suggesting a generalized prediction.

This study aimed to measure the levels of volatile sulfur compounds and investigate the occurrence of halitosis in patients with gingivitis and periodontitis. Additionally, the incidence rates of gingivitis and periodontitis in patients with halitosis were investigated. Through various statistical analyses, we attempted to determine the relationship between periodontal disease and halitosis. One-hundred-and-four participants (52 females and 52 males, mean age: 46.49 ± 16.03 years) were enrolled in this cross-sectional study, comprising 33 healthy controls, 43 patients with gingivitis, and 28 patients with periodontitis. Gas chromatography was used to measure hydrogen sulfide (H₂S) and methyl mercaptan (CH₃SH), which are representative VSCs. The VSC cut-off values for diagnosing halitosis were 65.79 ppb for women and 79.94 ppb for men. Total VSC level was significantly higher in the gingivitis than the healthy control group (186.72 ± 374.83 ppb vs. 19.80 ± 40.19 ppb, p = 0.035). There was no significant difference between the gingivitis and periodontitis (153.79 ± 278.51 ppb) groups. H₂S level was significantly higher in the gingivitis (100.51 ± 183.69 ppb) and periodontitis (91.57 ± 132.06 ppb) groups than in healthy controls (14.97 ± 31.22 ppb), and CH₃SH level was significantly higher in gingivitis group (29.31 ± 59.16 ppb) than in the healthy control (5.73 ± 14.10 ppb) (all p < 0.05). Halitosis was found in 3% of healthy controls and 39.5% and 42.9% of patients with gingivitis and periodontitis patients, respectively, making it significantly higher in the gingivitis and periodontitis groups than the healthy controls (p = 0.005). Conversely, among participants with halitosis, 53.1% had gingivitis, 37.5% had periodontitis, and 90.6 incidence had periodontal disease. Multivariate logistic regression analysis to predict the presence of halitosis, found periodontal disease was a significant predictor of halitosis (OR = 3.607, 95% CI 1.023–12.718, p = 0.046). Considering area under curve value for halitosis, the cut-off value of healthy control (H₂S:61.5 ppb, CH₃SH:3.5 ppb), gingivitis (H₂S:50.0 ppb, CH₃SH:6 ppb), and periodontitis (H₂S:62.0 ppb, CH₃SH:3.5 ppb) were (all p < 0.05). Our results emphasize the close and strong relationship between periodontal disease and halitosis through human clinical evidence based on the high co-occurrence rate of mutual diseases. Additionally, the presence of periodontal disease increased the probability of halitosis by 3.607 times. These results suggest that H₂S can be used as a biomarker of halitosis in patients with periodontal disease.

Objectives: This study aimed to investigate and compare sleep quality between patients with chronic temporomandibular disorder and healthy controls, and to analyze the association of sleep quality with disease characteristics, obstructive sleep apnea risk factors, and excessive daytime sleepiness. Methods: Chronic temporomandibular disorder patients (n = 503, mean age: 33.10 ± 13.26 years, 333 females) and 180 age- and sex-matched healthy controls (mean age: 32.77 ± 12.95 years, 116 females) were included, who completed well-organized clinical report and answered questions on sleep quality (Pittsburgh Sleep Quality Index), sleep apnea risk factors (STOP-Bang questionnaire), and excessive daytime sleepiness (Epworth sleepiness scale). Results: Mean global Pittsburgh Sleep Quality Index scores were significantly higher in the patients (6.25 ± 2.77) than in healthy controls (3.84 ± 2.29) (p < 0.001). Poor sleep was significantly more prevalent in the patient group (56.9%) than in healthy controls (22.2%) (p < 0.001). Compared with healthy controls, chronic temporomandibular disorder patients had a higher likelihood of obstructive sleep apnea (STOP-Bang total score ≥ 3; 7.2% vs. 16.1%; p < 0.01) and higher excessive daytime sleepiness (Epworth sleepiness scale score ≥ 10; 12.8% vs. 19.7%; p < 0.05). Age (odds ratio = 2.551; p < 0.001), female sex (odds ratio = 1.885; p = 0.007), total Epworth sleepiness scale score (odds ratio = 1.839; p = 0.014), and headache attributed to temporomandibular disorder (odds ratio = 1.519; p = 0.049) were the most powerful predictors of poor sleep (global Pittsburgh Sleep Quality Index score ≥ 5) in chronic temporomandibular disorder patients. Conclusion: Chronic temporomandibular disorder patients had markedly impaired sleep quality than healthy controls. Poorer sleep in patients with chronic temporomandibular disorder was associated with a variety of clinical factors, including a higher likelihood of excessive daytime sleepiness, older age, female gender, higher Epworth sleepiness scale scores, and the presence of headache attributed to temporomandibular disorder.